Mohamed Abdellahi Amar
Papers
2
Total Citations
4
H-Index
2
About
Mohamed Abdellahi Amar is a researcher focused on combinatorial optimization and high-performance computing, with a particular emphasis on solving the Probabilistic Traveling Salesman Problem (PTSP). His major contribution lies in developing a novel parallel Tabu Search algorithm that leverages both OpenMP and MPI environments to efficiently tackle PTSP instances. By integrating probabilistic elements into deterministic models, his work demonstrates how real-world problems—such as logistics, routing, and network design—can be more accurately represented and solved. His 2020 paper, which has garnered 2 citations, reviews key findings in the literature and proposes a new modeling approach for PTSP, highlighting the practical benefits of incorporating uncertainty into optimization. This work showcases his ability to bridge theoretical advances with tangible applications, making his research valuable for students and practitioners in operations research and parallel computing. Amar’s contributions underscore the importance of hybrid parallel strategies in addressing computationally intensive problems, offering a foundation for future studies in stochastic optimization and distributed algorithms.
Research Focus
Key Achievements
Top Papers
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